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A Novel Enhanced Ensemble Clustering Techniques in Machine Learning and Data Mining


Sajja Radharani, Amar Jukuntla, MadhuBabu Chevuru, P Amarnatha Reddy
Abstract

In recent years one of the most active research areas in data mining and machine learning is unsupervised learning. The objective of unsupervised learning is to model the essential structure or division in the facts in order to learn more about the data. In data mining clustering techniques very popular. DBSCAN is a type of partition grouping technique. The density-based clustering technique has played an essential role in the search for non-linear forms based on density. But, DBSCAN does not work well when dealing with groups of "variable density and high-dimensional data. It is sensitive to clustering parameters such as MinPts and Eps values. To overcome this we are using the OPTICA technique. The DBSCAN technique takes a long time for grouping formation. To improve this problem in OPTICS clustering algorithm.

Volume 11 | Issue 5

Pages: 597-603